Strategies and policies for sustainable development of Vietnam’s cultural industries using SWOT, AHP and QSPM approaches: A case study of the cultural tourism sector
Bibliographic record
Abstract
Sustainable development strategies and policies for Vietnam’s cultural industries and cultural tourism play a crucial role in promoting distinctive cultural values and simultaneously fostering a country's economic and social development. The main aim of this paper is to propose sustainable development strategies and policies for cultural tourism within Vietnam’s cultural industries by applying a combined approach. The methodology integrates qualitative Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis and quantitative methods, including the Analytical Hierarchy Process (AHP) method and the Quantitative Strategic Planning Matrix (QSPM), to evaluate internal and external factors influencing sustainable development of cultural tourism in Vietnam. Two hundred twenty-six survey responses from tourists, cultural tourism site managers, and 35 expert opinions were collected and analyzed. The findings identify the most significant strengths, weaknesses, opportunities, and threats impacting the sustainable development of cultural tourism in Vietnam. Among these, the strengths and opportunities outweigh the weaknesses and threats. Based on the analysis from the SWOT-AHP-QSPM model, the study discusses and develops a growth-oriented strategy, prioritizing the application of digital technology in cultural tourism services, enhancing tourists' experiences, improving service quality, and strengthening cultural tourism promotion campaigns. The preliminary findings provide insights for policymakers, cultural tourism service providers, and local communities to adopt policies and strategic solutions that will promote the sustainable development of cultural tourism in the future, contributing to the growth of Vietnam's cultural industries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".